Optimal tuning of multi-machine power system stabilizers by the Queen-Bee Evolution technique

Abstract

A Queen-Bee Evolution-based tuning of a multi-machine power system stabilizer, which aims at enhancing the damping of the system over a wide range of operating conditions, is introduced in this paper. The basic components of this study are modeled by IEEE Model 1.1 and Heffron–Philips constants, considering the external resistance but neglecting the armature resistance. The problem is then formulated as a constrained multi-objective optimization problem. For investigation purposes, two different test systems are considered. The non-linear time domain simulations of the proposed power system stabilizer are compared with that based on the conventional approach power system stabilizer. The overall transfer function and the characteristic equation of the compensated system are derived from the single equivalent block diagram, and the nature of stability is verified by both frequency response analysis and Routh-Hurwitz (RH) criterion. Simulation results show that the proposed method motivates faster damping and leads to minimal overshoots in both speed and power angle deviations.

A new optimization approach for multi-machine power system stabilizer design using a smart bacteria foraging algorithm

“…Artificial intelligence (AI) techniques evolutionary algorithms with a higher degree of robustness and adaptability such as the genetic algorithm (GA), 12 particle swarm optimization (PSO), 1 bacterial foraging algorithm (BFA), 13 queen-bee evolution technique, 14 evolutionary algorithms, 15 ant colony optimization, 16 simulated annealing, 17 tabu search algorithm 18 and chaotic optimization algorithms (COA) 19 have also been used for designing the parameters of CPSS.…”